code: def print_results(clusters: List[Clusters], type: str):
      print("-=" + type + " Clusters=-")
      for topic_cluster in clusters:
          print("\n\tTopic=" + topic_cluster.topic_id)
          for cluster in topic_cluster.clusters_list:
              cluster_mentions = list()
              for mention in cluster.mentions:
                  mentions_dict = dict()
                  mentions_dict["id"] = mention.mention_id
                  mentions_dict["text"] = mention.tokens_str
                  cluster_mentions.append(mentions_dict)
  
              print(
                  "\t\tCluster("
                  + str(cluster.coref_chain)
                  + ") Mentions="
                  + str(cluster_mentions)
              )
  
file_path: nlp-architect/examples/cross_doc_coref/cross_doc_coref_sieves.py
imports: import logging
  from typing import List
  from nlp_architect import LIBRARY_ROOT
  from nlp_architect.common.cdc.cluster import Clusters
  from nlp_architect.common.cdc.topics import Topics
  from nlp_architect.data.cdc_resources.relations.referent_dict_relation_extraction import ReferentDictRelationExtraction
  from nlp_architect.data.cdc_resources.relations.relation_types_enums import RelationType
  from nlp_architect.data.cdc_resources.relations.wikipedia_relation_extraction import WikipediaRelationExtraction
  from nlp_architect.data.cdc_resources.relations.word_embedding_relation_extraction import WordEmbeddingRelationExtraction
  from nlp_architect.models.cross_doc_coref.sieves_config import EventSievesConfiguration, EntitySievesConfiguration
  from nlp_architect.models.cross_doc_coref.sieves_resource import SievesResources
  from nlp_architect.models.cross_doc_coref.system.sieves_container_init import SievesContainerInitialization
  from nlp_architect.models.cross_doc_sieves import run_event_coref, run_entity_coref
label_desc: Use image node_id 4 for calling a global function with example usage: print_results(clusters, type) without return types